Reducing construction material cost by optimizing buy-in decision that accounts the flexibility of non-critical activities

Abstract

Purpose The goal of making buy-in decisions is to purchase materials at the right time with the required quantity and a minimum material cost (MC). To help achieve this goal, the purpose of this paper is to find a way of optimizing the buy-in decision with the consideration of flexible starting date of non-critical activities which makes daily demand adjustable. Design/methodology/approach First, a specific algorithm is developed to calculate a series of demand combinations modeling daily material demand for all the possible start dates. Second, future material prices are predicted by applying artificial neural network. Third, the demand combinations and predicted prices are used to generate an optimal buy-in decision. Findings By comparing MC in situation when non-critical activities always start at the earliest date to that in situations when the starting date is flexible, it is found that making material buy-in decision with the consideration of the flexibility usually helps reduce MC. Originality/value In this paper, a material buy-in decision-making method that accounts non-critical activities’ flexible starting date is proposed. A ternary cycle algorithm is developed to calculate demand combinations. The results that making material buy-in decision considering non-critical activities’ flexible starting date can reduce MC in most times indicates that contractors may consider non-critical activities’ flexibility a part of the buy-in decision-making process, so as to achieve an MC decrease and profit increase.

Efficiency estimation and reduction potential of the Chinese construction industry via SE-DEA and artificial neural network

“…The working logic of ANN, as a multilayer feedback algorithm, is to simulate the biological behavior of neurons on the basis of pattern recognition and reasoning, thereby enabling the capture and analysis of nonlinear and complex data (Okwu, 2019). One of the widely used methods in recent years is back-propagation (BP) ANN (BPANN), which is superior in the prediction and categorization of nonlinear systems (Meng et al , 2018). Figure 1 shows the structural framework of BPANN model.…”

“…Given the fact that the construction sector is highly complicated and unpredictable that a set of uncertainties regarding labor productivity, cost and quantity. Thereby, ANN models have also been applied in the construction industry to measure and predict construction costs (Meng et al , 2018), productivity (Mirahadi and Zayed, 2016), safety (Zhang et al , 2019) and risk (Islam et al , 2017). ANN models are regarded as a complement for the DEA to estimate the efficiency performance, which have been gradually conducted in current researches.…”

Considerations and important aspects for project management

“…However, as these are related to the purchasing and procurement processes, it is necessary to plan and manage purchases effectively, evaluate suppliers, compare quotes, negotiate favorable conditions, manage contracts, and ensure that the goods and services being purchased are high quality and available (Dixit et al, 2013;Meng et al, 2018). In addition, the acquisition of the materials, equipment and supplies necessary to carry out the project must be analyzed and planned, this includes evaluating suppliers, negotiating contracts, managing inventories, and ensuring that the required resources are available on time.…”

Core Elements Underlying Supply Chain Management in the Construction Industry: A Systematic Literature Review

“…Even thoguh time and material buffers are commonly used to mitigate these risks, they may contribute to significant time waste and enormous additional costs [3,21]. Therefore, planning and control of material flows, involving the mechanisms to ensure the availability of materials at the right time, with the required quantity and minimum cost [48], are critical to the success of a project. Accordingly, many models to support decision making for tasks, such as selection of material sources, determination of the number of deliveries and project scheduling, have been proposed by researchers [49][50][51][52][53][54].…”